Method and apparatus for determining signal-to-interference ratio with reduced bias effect

ABSTRACT

A method and apparatus for estimating a signal-to-interference ratio (SIR) of baseband signals which are received and processed by a data demodulator to provide demodulated signals to a SIR estimator. The SIR estimator receives the demodulated symbols from the data demodulator and estimates the average signal power of the demodulated symbols as a function of a median based average power value m d  and a mean based average power value m e  of the demodulated symbols for each quadrant of a quadrature phase shift keying (QPSK) constellation. The function is used to determine a minimum value m between m d  and m e . The SIR estimator estimates the average effective interference power of the demodulated symbols and calculates the SIR by dividing the estimated average signal power of the demodulated symbols by the estimated average effective interference power of the demodulated symbols. The SIR estimator reduces bias effects on SIR estimation.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application claims priority from U.S. Provisional Application No.60/425,367, filed on Nov. 8, 2002, which is incorporated by reference asif fully set forth.

FIELD OF THE INVENTION

The invention generally relates to a method and apparatus for findingthe signal-to-interference ratio (SIR) in a digital communicationsystem. More particularly, the invention relates to SIR estimation withreduced bias contribution.

BACKGROUND

A SIR measurement is an important metric of quality performance fordigital communication systems. For wireless communication systems suchas Third Generation (3G) wireless systems, SIR measurements are used inseveral link adaptation techniques such as transmit power control andadaptive modulation and coding. Typically, SIR measured at a receivingdevice is more meaningful than at a transmitting device because SIRmeasured at a receiving device directly reflects the quality ofcommunicated link signals, especially in the presence of multiple accessinterference or multipath fading channel.

By definition, a received signal consists of a desired signal andinterference. The interference may include other signals and thermalnoise at the receiving end. However, the receiving device does notgenerally have knowledge of either signal power or interference power sothat the receiving device needs to perform estimation of both signal andinterference power based on received signals using a blind method. Ablind method in SIR measurement for a given received signal refers tothe signal power and interference power (eventually SIR) obtained onlyfrom observation samples of the received signal without any trainingsequence or any prior knowledge of the desired received signal andinterference in the received signal.

There exist several approaches in performing measurement of receivedSIR. In the prior art, the signal power for a given signal is estimatedby averaging the received signal over time, and the interference poweris estimated by measuring total power of the received signal and thensubtracting the estimated signal power from the total power. The SIR isthen determined as the ratio between the estimated signal power andinterference power.

The SIR estimation for a given received signal can be performed atdifferent observation points of the receiver structure, such as at thereceiver antenna end, at the input to the data demodulator, or at theoutput from the data demodulator. However, SIR estimates measured atdifferent locations usually have different levels of accuracy becausethe signal gain or the interference amount at one measurement locationis likely to be different from the readings at other locations.

The main problem in measuring the SIR of data signals is that an SIRestimate is likely to deviate from the corresponding true SIR value.Such inaccuracies in SIR estimation arise due to the following two mainreasons. First, a signal and its interference cannot be completelyseparated. Second, desired signals are generally data-modulated, so thatthe SIR estimation is done in a “blind” way, i.e., without priorknowledge of the data signal. This increases uncertainties in estimatingsignal power.

In many prior art systems, SIR estimation mainly relies on a mean filterto calculate signal and noise power, resulting in undesirably large biascontribution. Generally, SIR estimation becomes more overestimated asSIR values are smaller, due primarily to a larger bias contribution.

Typically, the k-th demodulated symbol, y_(k), as an input to ademodulator based SIR estimator, can be represented by:

 y _(k) =s _(k) ^(d) +n _(k) ^(e)  Equation (1)

where S_(k) ^(d) denotes the k-th demodulated desired QPSK signal andn_(k) ^(e) denotes the total effective interference (including residualintra-cell interference, inter-cell interference and background noiseeffects), respectively. S refers to the signal and d is the desiredsignal. The SIR is then estimated in terms of the average signal power,P_(s), and the effective interference power, P_(I) as: $\begin{matrix}{{SIR} = {\frac{P_{S}}{P_{I}} = \frac{E\left\{ {s_{k}^{d}}^{2} \right\}}{E\left\{ {n_{k}^{e}}^{2} \right\}}}} & {{Equation}\quad(2)}\end{matrix}$

By comparing Equation (2) to the SIR definition used in 3GPP, (i.e.,RSCP*SF/Interference), neither RSCP nor ISCP is explicitly evaluated forthe measurement. In other words, Equation (2) expresses the SIRmeasurement of a DPCH more explicitly than the 3GPP definition, Inaddition, since the SIR measurement is carried out on the data part ofthe received signal, a blind estimation is required due to the unknowntransmit data at the receiving device. The function “E{ }” used hereinrepresents an operator to estimate the statistical average (or expectedor mean) value of a variable within the brackets “{ }”. In the contextof probability/statistics or communication systems, it is widelyconventional to use E{x} to define the average (expected) value of a(random) variable x.

While the SIR definition used in 3GPP is implicitly independent of thedata demodulator type used in the receiving device, the SIR measurementin Equation (2) is implemented at the demodulator output. Thus, the SIRgiven in Equation (2) is likely to be different for differentdemodulator types. For example, for a given received signal primarilycorrupted by interference, the SIR measured at a conventional matchedfilter receiver is likely to be smaller than that at an advancedreceiver, such as an interference canceller, due to reduced interferenceeffects. Note that the SIR at the demodulator output is the primarydeterminant of communication link performance. However, the SIRmeasurement on the data portion of the received signal must deal withthe unknown transmit data.

FIG. 1 depicts a typical transmitted QPSK signal constellation whereE_(s) represents the transmitted QPSK symbol energy. For wirelesssystems such as 3GPP systems, after spreading the QPSK signal, theresulting spread signal arrives through a radio channel at the receiver.The received signal is then processed by the demodulator, which providesthe demodulated symbols, y_(k) for k=1, 2, . . . , N_(burst), whereN_(burst) is the number of symbols in the data burst of the receivedsignal.

Taking into account the fading channel impact and demodulator gain, inthe absence of the effective interference, the typical signalconstellation of soft-valued demodulated symbols can be observed onaverage, as shown in FIG. 2 where S_(m) represents the m-th demodulatedsignal symbol.

In the presence of interference, the typical demodulator output symbolscan be represented pictorially as in FIG. 3. For a given transmittedsymbol, S_(k), its output symbol may fall into any point in the QPSKconstellation, centering around the associated average demodulatedsymbol, E{S_(k) ^(d)}. In this case, the blind-based average powerestimation on the demodulator output would be performed. When thedecision for each demodulated symbol is made as to which symbol wassent, some decision error may occur most likely due to the effectiveinterference and fading channel. For example, as shown in FIG. 3, eventhough S₂ ^(d) was actually sent for the k-th symbol, the interferencemay cause the demodulator output symbol, marked by y_(k), to becomecloser to S₁ ^(d) in the 1^(st) quadrant than the actually transmittedsymbol, S₂ ^(d). As a result, an incorrect decision (i.e., a decisionerror) on y_(k) may be made. The decision error is the main source oferror that causes the average signal power estimate, and consequentlythe SIR estimate to be overestimated. In lower SIR range (high raw BERrange), the average signal power (or SIR) estimate is likely to be moreoverestimated.

It is therefore desirable to provide a method of performing SIRestimation without experiencing the disadvantages of prior art methods.

SUMMARY

The present invention provides a method and apparatus for SIR estimationwhich is more accurate than the estimation obtained by prior artmethods. In an exemplary embodiment, the present invention implements amethod, which preferably uses the demodulator output for the SIRestimation, noting that the primary determinant of communication linkperformance appears to be the SIR at the output of the data demodulator.The method reduces such bias contribution in SIR estimation so that SIRestimates are as close to true SIR as possible.

The inventive method preferably uses both a median filter and a meanfilter and combines outputs from the median filter and mean filter inSIR estimation. In addition, advantageously, a correction term as afunction of the mean and median values is introduced to further mitigatethe bias effect.

In accordance with a preferred embodiment of the present invention, anestimate of SIR of baseband signals which are received and processed bya data demodulator to provide demodulated signals to a SIR estimator isperformed. The SIR estimator receives the demodulated symbols from thedata demodulator and estimates the average signal power of thedemodulated symbols as a function of a median based average power valuem_(d) and a mean based average power value m_(e) of the demodulatedsymbols for each quadrant of a quadrature phase shift keying (QPSK)constellation. The function is used to determine a minimum value mbetween m_(d)and m_(e). The SIR estimator estimates the averageeffective interference power of the demodulated symbols and calculatesthe SIR by dividing the estimated average signal power of thedemodulated symbols by the estimated average effective interferencepower of the demodulated symbols. The SIR estimator reduces bias effectson SIR estimation.

BRIEF DESCRIPTION OF THE DRAWINGS

A more detailed understanding of the invention may be had from thefollowing description of a preferred embodiment, to be understood inconjunction with the accompanying drawing wherein:

FIG. 1 shows a typical signal constellation for transmitted QPSKsymbols;

FIG. 2 shows a typical signal constellation for averaged demodulatedQPSK symbols;

FIG. 3 is a typical space representation of demodulated symbols in thepresence of interference;

FIG. 4 is an exemplary functional block diagram of a SIR estimatoroperating in accordance with a preferred embodiment of the presentinvention;

FIG. 5 is a system block diagram including the SIR estimator of FIG. 4;and

FIG. 6 is a flow chart including method steps implemented by the SIRestimator of FIG. 4.

ACRONYMS

-   -   3GPP: third generation partnership project    -   BER: block error rate    -   BPSK: binary phase shift keying    -   DPCH: dedicated physical channel    -   ISCP: interference signal code power    -   MUD: multi-user detection    -   PSK: phase shift keying    -   QAM: quadrature amplitude modulation    -   QPSK: quadrature phase shift keying    -   RSCP: received signal code power    -   SF: spreading factor    -   SIR: signal-to-interference ratio    -   UE: user equipment

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

A preferred embodiment of the present invention as described below,provides a novel SIR estimation process based on a data demodulatoroutput. The present invention also provides an SIR estimation apparatus.By definition, the term data demodulator output is understood to meanthe output provided at the final stage of the considered datademodulator. The data demodulator processes received baseband signalsand provides soft-valued estimates of transmitted symbols. The estimatedsymbols are further processed by other receiver functions, such as achannel decoder, in order for the receiving device to extract thetransmitted data information.

In the context of a 3GPP system, the demodulator may be configured as amulti-user detection (MUD) receiver or a single-user detection (SUD)receiver, such as a matched filter, a Rake receiver and an equalizer.Even though BPSK (binary phase shift keying) and QPSK (quadrature phaseshift keying) modulation schemes are referenced in the preferredembodiments, the present invention may be applied to higher ordermodulations like 8-PSK and 16-QAM (quadrature amplitude modulation).

The present invention estimates average signal power such that the biaseffect is mitigated. When QPSK symbols are transmitted, the averagesignal power of the k-th demodulated signal, y_(k), may be estimated asfollows: $\begin{matrix}{{{{E\left\{ {S_{k}^{d}}^{2} \right\}} \approx {{\frac{1}{4}{\sum\limits_{Q_{i}}\quad\left. {{E\left\{ y_{k} \right.y_{k}} \in Q_{i}} \right\}}}}}}^{2} = {{\quad{{{\frac{1}{4}\sum\limits_{Q_{i}}}}\frac{1}{N_{Qi}}{\sum\limits_{k = 1}^{N_{Qi}}\quad{y_{k}\left( Q_{i} \right)}}}}^{2} = {{\frac{1}{4}{\sum\limits_{Q_{i}}{{E\left\{ {y_{k}\left( Q_{i} \right)} \right\}}}}}}^{2}}} & {{Equation}\quad(3)}\end{matrix}$where S_(k) ^(d) is the k-th demodulated desired QPSK signal, Q_(i)represents the i^(th) quadrant in the QPSK signal constellation; N_(Qi),represents the number of the demodulator output symbols belonging to thei^(th) quadrant region after making blind based symbol decisionsrespectively; and y_(k)(Q_(i)) is the k-th output symbol, which is inthe i^(th) quadrant.

Equation (3) is utilized to determine the mean of the demodulator outputsymbols in each quadrant of the QPSK constellation. Secondly, Equation(3) determines the average signal power based on the magnitude of themean signal points in the individual quadrant. This two-step averaging(mean) mechanism may provide a good estimate for the average signalpower in relatively high SIR range (equivalently low symbol error rate).However, as mentioned previously, as the actual SIR gets lower, theaverage signal power values become biased (overestimated) due to moresymbol decision errors, leading to overestimated SIR values (seeEquation (2)) as well. To reduce the bias effect in the signal powerestimation, another statistical parameter, called “median” (the middleof a distribution), is utilized as will be described in detailhereinafter.

The mean and median are symmetrically distributed. Accordingly, withhigh SIR values, the mean and median of the demodulator output symbolslocated in each quadrant are almost identical since the interferenceexperienced in the individual quadrant can be approximated to benormally distributed in a high SIR range.

The median is less sensitive to extreme sample values than the mean.This characteristic of the median may make the median based averagepower closer to the true average power than the mean based average powerespecially for a highly skewed distribution such as a Log-normaldistribution, or as the SIR gets lower and the distribution of thedemodulator output samples in each quadrant approaches the skeweddistribution.

The standard deviation of the median for large samples with normaldistribution is larger than that of the mean. The median is thus moresubject to sampling fluctuations. Thus, when the number of randomsamples with normal distribution is large, the standard deviation of themedian is generally greater than that of the mean.

Taking into account the above statistical properties of the median andmean, the present invention determines an average signal power estimateE{ } of the symbols/bits as a function of the minimum value between themedian value and mean value as follows: $\begin{matrix}{{E\left\{ {S_{k}^{d}}^{2} \right\}} = {{\min\left( {\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack,\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack} \right)}}^{2}} & {{Equation}\quad(4)}\end{matrix}$where S_(k) ^(d) is the k-th demodulated desired QPSK signal, y_(k) isthe k-th demodulated symbol, Q_(i) denotes the quadrants i of the QPSKconstellation, median(y_(k)(Q_(i))) and mean(y_(k)(Q_(i))) denote themedian and mean values, respectively, of the symbols/bits in the i-thquadrant Q_(i), and min([median value], [mean value]) represents aminimum value function for determining a minimum value between themedian and mean values. y_(k)(Q_(i)) is a complex valued symbol,median(y_(k)(Q_(i)))=median(real(y_(k)(Q_(i))))+j·median(imag(y_(k)(Q_(i)))),and similarlymean(y_(k)(Q_(i)))=mean(real(y_(k)(Q_(i))))+j·mean(imag(y_(k)(Q_(i)))).That is, the average signal power is equal to the magnitude squared ofthe minimum of the median absolute and mean absolute averaged over allquadrants. In Equation (4), the main reason for finding the minimumvalue between the median value and mean value of the demodulated symbolsis described below. The main reason for using the minimum value betweenthe median and mean value is to reduce the bias effect. It should benoted that the SIR value estimated according to the present inventioncan not be greater than the SIR derived by the mean value only, becausethe minimum value between the median and mean value is the smallest oneof the median value and the mean value.

The selection of the median value as the minimum mitigates the biaseffect on estimating average signal power especially in the low SIRrange. On the other hand, selection of the mean value as the minimumcompensates for the drawback in median calculation, such as beingsubject to the sampling fluctuations. Thus, by effectively combining themedian and mean values of the demodulator output symbols for eachquadrant of the QPSK constellation, the estimation performance of theaverage signal power is substantially improved. Even though the minimumvalue between the median value and the mean value is referenced in thepreferred embodiments, other combined values from the median and meanmay be used to determine the average signal power estimate. For example,a weighted (combined) method is as follows: $\begin{matrix}{{E\left\{ {S_{k}^{d}}^{2} \right\}} = {{{\alpha \cdot \left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack}\quad} + {{\quad{\quad\quad}\quad}\quad{\quad\quad}\quad{\quad{\left( {1 - \alpha} \right) \cdot {\quad\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack }^{2}}}}}} & {{Equation}\quad(5)}\end{matrix}$where 0<=α<=1.

Next, the averaged effective interference power based on Equation (4) isestimated. From Equations (1) and (4), the averaged effectiveinterference power may be expressed by: $\begin{matrix}{{E\left\{ {n_{k}^{e}}^{2} \right\}} = {\frac{1}{4}\left\{ {\sum\limits_{i = 1}^{4}\quad{\frac{1}{N_{Q_{i}}}{\sum\limits_{k = 1}^{N_{Q_{i}}}\quad{{{y_{k}\left( Q_{i} \right)} - {q_{i} \cdot \sqrt{E\left\{ {s_{k}^{d}}^{2} \right\}}}}}^{2}}}} \right\}}} & {{Equation}\quad(6)}\end{matrix}$where n_(k) ^(e) denotes the total effective interference, N_(Qi)represents the number of the demodulator output symbols belonging to thei^(th) quadrant region after making blind based symbol decisionsrespectively, y_(k)(Q_(i)) is the k-th output symbol, which is in thei^(th) quadrant, √{square root over (E{|s_(k) ^(d)|²})} represents theaverage signal amplitude estimate and q_(i), for i=1, 2, 3 and 4,respectively, represents the i-th QPSK constellation signal pointdenoted as follows:${q_{1} = \frac{1 + j}{\sqrt{2}}},{q_{2} = \frac{{- 1} + j}{\sqrt{2}}},{q_{3} = \frac{{- 1} - j}{\sqrt{2}}},{q_{4} = \frac{1 - j}{\sqrt{2}}},$where j is an imaginary number, (e.g. j=√{square root over (−1)}).

SIR estimation must now be performed. From Equations (4) and (6), thedemodulator based SIR estimate can be expressed by: $\begin{matrix}{{SIR} = {\frac{E\left\{ {s_{k}^{d}}^{2} \right\}}{E\left\{ {n_{k}^{e}}^{2} \right\}} = \frac{{\min\left( {\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack,\left. \quad\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack \right)} \right.}^{2}}{\frac{1}{4} \cdot \left\{ {\sum\limits_{i = 1}^{4}\quad{\frac{1}{N_{Q_{i}}} \cdot {\sum\limits_{k = 1}^{N_{Q_{i}}}{{{y_{k}\left( Q_{i} \right)} - {q_{i} \cdot \sqrt{E\left\{ {s_{k}^{d}}^{2} \right\}}}}}^{2}}}} \right\}}}} & {{Equation}\quad(7)}\end{matrix}$

This SIR estimation has been validated via link-level simulations, whichshow that the performance of the SIR estimation based on Equation (7) isacceptable in reasonable operating SIR range. But in the low SIR range,(for instance, from 5 dB to 0 dB or below), the biased effect due tosymbol errors still appears in the SIR estimation so that it causes theestimated SIR to deviate from true SIR. The minimum value can notcompletely eliminate the bias effect especially in the low SIR range,because there may be some symbol decision errors in determining theassociated blindly based symbol decision. In this case, some correctionon Equation (7) is required to meet the current standards requirement of3GPP Working Group 4 (WG4). The present invention (including thecorrection term) exceeds this requirement.

By a heuristic approach through Monte-Carlo simulations, a correctionterm is introduced in the numerator (signal power term) of the aboveequation as a function of the offset between the calculated median andmean values, as follows: $\begin{matrix}{{{SIR} = \frac{{{\min\left( {\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack,\left. \quad\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack \right)} \right.}^{2} - C}}{\frac{1}{4} \cdot \left\{ {\sum\limits_{i = 1}^{4}\quad{\frac{1}{N_{Q_{i}}} \cdot {\sum\limits_{k = 1}^{N_{Q_{i}}}{{{y_{k}\left( Q_{i} \right)} - {q_{i} \cdot \sqrt{E\left\{ {s_{k}^{d}}^{2} \right\}}}}}^{2}}}} \right\}}}\quad{where}\quad{C = {{{\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack - \left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack}}^{2}.}}} & {{Equation}\quad(8)}\end{matrix}$

The basis for using such a correction term is that in a high SIR range,the median value and mean value in the correction term are mostly likelyclose to each other. Therefore, the correction term can be negligiblewhen in the range it is not needed, (since without it the estimated SIRis already within the accuracy requirement). However, as the actual SIRgets lower, the skewed distribution due to symbol error effect of thedemodulator output samples may drive the correction term increasingly,since the difference between the corresponding median and mean valuesmay be gradually increased. At the same time, as the estimated signalpower (eventually SIR) gets overestimated (biased), the correction termmay help to reduce the biased effect in the SIR estimation.

Although the SIR measurement method described hereinbefore was derivedunder the assumption that the demodulator output is the complex valuedQPSK symbols sequence, for a practical MUD implementation, the MUDprovides real valued data bits sequences with each pair of twoconsecutive data bits, which can map to a complex valued symbol such asQPSK modulation in a transmitter.

FIG. 4 shows a block diagram of a SIR estimator 400 taking input as databits rather than QPSK symbols, in accordance with a preferred embodimentof the present invention. The SIR estimator includes an input port 405for receiving an input bit sequence, a hard limiter 410, a multiplier415, a median filter 420, a first mean filter 425, a minimizing processblock 430, a signal power process block 435, a correction term processblock 440, a second mean filter 445, a SIR calculating process block450, summers/comparators 455, 460 and a process block 465.

Input port 405 receives a soft-valued bit sequence. The SIR estimator400 processes absolute values of symbols input to the SIR estimator inthe form of a bit sequence received via input port 405. The bit sequenceis routed to the hard limiter 410 and the multiplier 415. The hardlimiter 410 provides a +1 bit to multiplier 415 if a soft-valued bit isgreater or equal to zero. Otherwise the hard limiter 410 provides a −1bit to the multiplier 415. Multiplier 415 then multiplies eachsoft-valued input bit, r_(i), by the corresponding hard limited bit,resulting in the absolute value |r_(i)| of the individual input bit.

The absolute value |r_(i)| indicates that a hard-bit decision on eachinput bit, r_(i), has been made and if the resulting bit decision turnsout to be −1, then the input bit is 180 degree phase shifted. Otherwise,the input bit remains unchanged. Thus, the calculation of the averagesignal power and interference power is based on a blind based bitdecision. The multiplier 415 outputs the absolute value |r_(i)| tomedian filter 420, first mean filter 425 and summer/comparator 460.Based on a running number of samples, the median filter 420 and meanfilter 425 determine the median value and mean value, respectively, ofthe absolute valued bit sequence. The outputs from the median filter 420(m_(d)) and first mean filter 425 (m_(e)) are compared in the minimizingprocess block 430 to determine a minimum value m between the medianbased average power value m_(d) and the mean based average power valuem_(e). The correction term process block 440 also receives the outputsfrom the median filter 420 (m_(d)) and mean filter 425 (m_(e)) andperforms the following calculation to determine a correction term C:C=|m _(d) −m _(e)|²,  Equation (9)where$m_{d} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}\quad{and}}}}$${m_{e} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}}},$y_(k) is the k-th symbol in the sequence of symbols, Q_(i) denotes thequadrants i of the QPSK constellation, and median(y_(k)(Q_(i))) andmean(y_(k)(Q_(i))) denote the median and mean values, respectively, ofthe symbols in the i-th quadrant Q_(i).

The output m of minimizing process block 430 is routed to the signalpower process block 435 and to summer/comparator 460. Thesummer/comparator 455 compares the output Ps of signal power processblock 435 to the correction term C, where:P _(s)=(m)²  Equation (10)

In order to determine the average interference power, the process block465 first receives the output of summer/comparator 460 and performs thefunction (|r_(i)|−m)² extracts the interference component out of theinput bit sequence. The second mean filter 445 receives the output ofprocess block 465 and outputs P_(N) to the SIR calculating process block450. The SIR is calculated by SIR calculating process block 450 based onthe output from summer/comparator 455 and second mean filter 445, where:$\begin{matrix}{{SIR} = \frac{P_{s} - C}{P_{N}}} & {{Equation}\quad(11)}\end{matrix}$

FIG. 5 shows a system 500 which includes a demodulator 505 and a knownsoft-symbol-to-soft-bit mapper 510 which inputs a soft-valued bitsequence to the SIR estimator 400. The SIR estimator 400 may be usedalso for higher order modulations like 8-PSK, 16-QAM, and 64-QAM, if thecomplex valued demodulated symbols are converted to soft-valued bits viathe soft-symbol-to-soft-bit mapper 510.

FIG. 6 is a flow chart of a method 600 implemented by the SIR estimator400. The SIR estimator estimates the SIR of symbols/bits by receivingthe symbols/bits (step 605), estimating the average signal power of thesymbols/bits as a function of a median based average power value m_(d)and a mean based average power value m_(e) of the symbols/bits for eachquadrant of a QPSK constellation (step 610), estimating the averageeffective interference power of the symbols/bits (step 615) andcalculating the SIR by dividing the estimated average signal power ofthe symbols/bits by the estimated average effective interference powerof the symbols/bits (step 620). The function of the median based averagepower value m_(d) and the mean based average power value m_(e) is toprovide a minimum value function for determining a minimum value mbetween the median based average power value m_(d) and the mean basedaverage power value m_(e). The average signal power of the symbols/bitsis equal to the magnitude squared of the minimum of the absolute valueof the median based average power value m_(d) and the absolute value ofthe mean based average power value m_(e) averaged over all of thequadrants of the QPSK constellation.

The foregoing describes a novel SIR estimator based preferably on datasymbols. Output of the data demodulator of interest, such as Rake outputor MUD output, is fed to the SIR estimator. As indicated above, anadvantage from using a data demodulator output as input is that thedemodulator output best and directly reflects quality of received datasignals. Especially when SIR measurement is used for link controltechniques like power control, a data-demodulator based SIR measurementas described above is highly desirable. In addition, the proposed SIRestimator is capable of reducing bias effects on SIR estimation,resulting in more reliable and accurate SIR estimation than the priorart. All such modifications and variations are envisaged to be withinthe scope of the invention.

While the present invention has been described in terms of the preferredembodiment, other variations which are within the scope of the inventionas outlined in the claims below will be apparent to those skilled in theart.

1. A method of estimating a signal-to-interference ratio (SIR) ofbaseband signals which are received and processed by a data demodulatorto provide demodulated symbols to a SIR estimator, the methodcomprising: (a) the SIR estimator receiving the demodulated symbols fromthe data demodulator; (b) the SIR estimator estimating the averagesignal power of the demodulated symbols as a function of a median basedaverage power value m_(d) and a mean based average power value m_(e) ofthe demodulated symbols for each quadrant of a quadrature phase shiftkeying (QPSK) constellation; (c) the SIR estimator estimating theaverage effective interference power of the demodulated symbols; and (d)the SIR estimator calculating the SIR by dividing the estimated averagesignal power of the demodulated symbols by the estimated averageeffective interference power of the demodulated symbols.
 2. The methodof claim 1 wherein the function of the median based average power valueand the mean based average power value is to provide a minimum valuefunction for determining a minimum value m between the median basedaverage power value m_(d) and the mean based average power value m_(e).3. The method of claim 2 wherein the average signal power of thedemodulated symbols is equal to the magnitude squared of the minimum ofthe absolute value of the median based average power value m_(d) and theabsolute value of the mean based average power value m_(e) averaged overall of the quadrants of the QPSK constellation.
 4. The method of claim 1wherein the demodulator is configured as a multi-user detection (MUD)receiver or a single-user detection (STUD) receiver.
 5. The method ofclaim 1 wherein the demodulated symbols are included in a burst of adedicated physical channel (DPCH).
 6. The method of claim 1 wherein thedemodulated symbols are QPSK data symbols.
 7. The method of claim 1wherein the demodulated symbols are binary phase shift keying (BPSK)data symbols.
 8. The method of claim 1 wherein step (b) further includesperforming the following calculation to determine an average signalpower estimate E{ } of the demodulated symbols where:${E\left\{ {S_{k}^{d}}^{2} \right\}} = {{\min\left( {\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack,\left. \quad\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack \right)} \right.}^{2}}$wherein S_(k) ^(d) is the k-th demodulated desired QPSK signal, y_(k) isthe k-th demodulated symbol, Q_(i) denotes the quadrants i of the QPSKconstellation, median (y_(k)(Q_(i))) and mean(y_(k)(Q_(i))) denote themedian and mean values, respectively, of the symbols in the i-thquadrant Q_(i), and min([median value], [mean value]) represents aminimum value function for determining a minimum value between themedian and mean values.
 9. The method of claim 8 wherein step (c)further includes performing the following calculation to determine theaverage effective interference power E{ } of the demodulated symbols:${{E\left\{ {n_{k}^{e}}^{2} \right\}} = {\frac{1}{4}\left\{ {\sum\limits_{i = 1}^{4}{\frac{1}{N_{Q_{i}}}{\sum\limits_{k = 1}^{N_{Q_{i}}}\quad{{{y_{k}\left( Q_{i} \right)} - {q_{i} \cdot \sqrt{E\left\{ {s_{k}^{d}}^{2} \right\}}}}}^{2}}}} \right\}}},$wherein n_(k) ^(e) denotes the total effective interference, N_(Q)represents the number of the demodulator output symbols belonging to thei^(th) quadrant region after making blind based symbol decisionsrespectively, y_(k)(Q_(i)) is the k-th output symbol, which is in thei^(th) quadrant. √{square root over (E{|S_(k) ^(d)|²})} represents theaverage signal amplitude estimate and q_(i), for i=1, 2, 3 and 4,respectively, represents the i-th QPSK constellation signal pointdenoted as follows:${q_{1} = \frac{1 + j}{\sqrt{2}}},{q_{2} = \frac{{- 1} + j}{\sqrt{2}}},{q_{3} = \frac{{- 1} - j}{\sqrt{2}}},{q_{4} = \frac{1 - j}{\sqrt{2}}},$where j is an imaginary number.
 10. The method of claim 9 wherein step(d) further includes performing the following calculation to determinethe SIR of the demodulated symbols:${{SIR} = \frac{{{\min\left( {\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack,\left. \quad\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack \right)} \right.}^{2} - C}}{\frac{1}{4} \cdot \left\{ {\sum\limits_{i = 1}^{4}{\frac{1}{N_{Q_{i}}} \cdot {\sum\limits_{k = 1}^{N_{Q_{i}}}\quad{{{y_{k}\left( Q_{i} \right)} - {q_{i} \cdot \sqrt{E\left\{ {s_{k}^{d}}^{2} \right\}}}}}^{2}}}} \right\}}},$where C is a correction term determined by performing the followingcalculation:$C = {{{\left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack - \left\lbrack {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}} \right\rbrack}}^{2}.}$11. The method of claim 9 wherein step (d) further includes performingthe following calculation to determine a correction term C:C=|m_(d)−m_(e)|² where$m_{d} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}\quad{and}}}}$$m_{e} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}.}}}$12. A method of estimating a signal-to-interference ratio (SIR) of asequence of data symbols, the method comprising: (a) receiving thesequence of data symbols; (b) estimating the average signal power of thesequence of symbols as a function of a median based average power valuem_(d) and a mean based average power value m_(e) of the symbols; (c)estimating the average effective interference power of the sequence ofsymbols; and (d) calculating the SIR by dividing the estimated averagesignal power of the sequence of symbols by the estimated averageeffective interference power of the sequence of symbols.
 13. The methodof claim 12 wherein the function of the median based average power valueand the mean based average power value is to provide a minimum valuefunction for determining a minimum value m between the median basedaverage power value m_(d) and the mean based average power value m_(e).14. The method of claim 13 wherein the average signal power of thesequence of symbols is equal to the magnitude squared of the minimum ofthe absolute value of the median based average power value m_(d) and theabsolute value of the mean based average power value m_(e) averaged overall of the quadrants of a quadrature phase shift keying (QPSK)constellation.
 15. The method of claim 14 wherein step (d) furtherincludes performing the following calculation to determine a correctionterm C: C=|m_(d)−m_(e)|², where$m_{d} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{{median}\left( {y_{k}\left( Q_{i} \right)} \right)}}\quad{and}}}}$${m_{e} = {\frac{1}{4} \cdot {\sum\limits_{i = 1}^{4}\quad{{{mean}\left( {y_{k}\left( Q_{i} \right)} \right)}}}}},$y_(k) is the k-th symbol in the sequence of symbols, Q_(i) denotes thequadrants i of the QPSK constellation, and median(y_(k)(Q_(i))) andmean(y_(k)(Q_(i))) denote the median and mean values, respectively, ofthe symbols in the i-th quadrant Q_(i).